Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene BIN1 Is Insufficient to Drive Cognitive or Network Excitability Changes.

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[1] § Materials and Methods › Behavioral tests › Pose estimation ↔ keypoint_moseq/io.py, lines 46 to 169 · score 0.69 · Pose trajectories, principal component, video recorded, anterior, posterior, Keypoint MoSeq · Report a mismatch
[2] § Materials and Methods › Behavioral tests › Pose estimation ↔ keypoint_moseq/util.py, lines 513 to 630 · score 0.59 · principal component, Keypoint MoSeq, PCA, trajectories, Pose, model · Report a mismatch

Paper

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The authors' code

Python · 1,428 lines · 52 KB · other · 1 match

import jax.numpy as jnp
import jax
import re
import commentjson
import json
import numpy as np
import h5py
import joblib
import tqdm
import yaml
import os
import pandas as pd
from textwrap import fill
import sleap_io
from pynwb import NWBHDF5IO
from ndx_pose import PoseEstimation
from itertools import islice
from keypoint_moseq.util import list_files_with_exts, check_nan_proportions
from jax_moseq.utils import get_frequencies, unbatch
from scipy.io import loadmat
def _build_yaml(sections, comments):
text_blocks = []
for title, data in sections:
centered_title = f" {title} ".center(50, "=")
text_blocks.append(f"\n\n{'#'}{centered_title}{'#'}")
for key, value in data.items():
text = yaml.dump({key: value}).strip("\n")
if key in comments:
text = f"\n{'#'} {comments[key]}\n{text}"
text_blocks.append(text)
return "\n".join(text_blocks)
def _get_path(project_dir, model_name, path, filename, pathname_for_error_msg="path"):
if path is None:
assert project_dir is not None and model_name is not None, fill(
f"`model_name` and `project_dir` are required if `{pathname_for_error_msg}` is None."
)
path = os.path.join(project_dir, model_name, filename)
return path
def generate_config(project_dir, **kwargs):
"""Generate a `config.yml` file with project settings. Default settings
will be used unless overriden by a keyword argument.
Parameters
----------
project_dir: str
A file `config.yml` will be generated in this directory.
kwargs
Custom project settings.
"""
def _update_dict(new, original):
return {k: new[k] if k in new else v for k, v in original.items()}
hypperams = _update_dict(
kwargs,
{
"error_estimator": {"slope": -0.5, "intercept": 0.25},
"obs_hypparams": {
"sigmasq_0": 0.1,
"sigmasq_C": 0.1,
"nu_sigma": 1e5,
"nu_s": 5,
},
"ar_hypparams": {
"latent_dim": 10,
"nlags": 3,
"S_0_scale": 0.01,
"K_0_scale": 10.0,
},
"trans_hypparams": {
"num_states": 100,
"gamma": 1e3,
"alpha": 5.7,
"kappa": 1e6,
},
"cen_hypparams": {"sigmasq_loc": 0.5},
},
)
hypperams = {k: _update_dict(kwargs, v) for k, v in hypperams.items()}
anatomy = _update_dict(
kwargs,
{
"bodyparts": ["BODYPART1", "BODYPART2", "BODYPART3"],
"use_bodyparts": ["BODYPART1", "BODYPART2", "BODYPART3"],
"skeleton": [
["BODYPART1", "BODYPART2"],
["BODYPART2", "BODYPART3"],
],
"anterior_bodyparts": ["BODYPART1"],
"posterior_bodyparts": ["BODYPART3"],
},
)
other = _update_dict(
kwargs,
{
"recording_name_suffix": "",
"verbose": False,
"conf_pseudocount": 1e-3,
"video_dir": "",
"keypoint_colormap": "autumn",
"whiten": True,
"fix_heading": False,
"fps": 30,
"outlier_scale_factor": 6.0
},
)
fitting = _update_dict(
kwargs,
{
"added_noise_level": 0.1,
"PCA_fitting_num_frames": 1000000,
"conf_threshold": 0.5,
# 'kappa_scan_target_duration': 12,
# 'kappa_scan_min': 1e2,
# 'kappa_scan_max': 1e12,
# 'num_arhmm_scan_iters': 50,
# 'num_arhmm_final_iters': 200,
# 'num_kpslds_scan_iters': 50,
# 'num_kpslds_final_iters': 500
},
)
comments = {
"verbose": "whether to print progress messages during fitting",
"keypoint_colormap": "colormap used for visualization; see `matplotlib.cm.get_cmap` for options",
"added_noise_level": "upper bound of uniform noise added to the data during initial AR-HMM fitting; this is used to regularize the model",
"PCA_fitting_num_frames": "number of frames used to fit the PCA model during initialization",
"video_dir": "directory with videos from which keypoints were derived (used for crowd movies)",
"recording_name_suffix": "suffix used to match videos to recording names; this can usually be left empty (see `util.find_matching_videos` for details)",
"bodyparts": "used to access columns in the keypoint data",
"skeleton": "used for visualization only",
"use_bodyparts": "determines the subset of bodyparts to use for modeling and the order in which they are represented",
"anterior_bodyparts": "used to initialize heading",
"posterior_bodyparts": "used to initialize heading",
"trans_hypparams": "transition hyperparameters",
"ar_hypparams": "autoregressive hyperparameters",
"obs_hypparams": "keypoint observation hyperparameters",
"cen_hypparams": "centroid movement hyperparameters",
"error_estimator": "parameters to convert neural net likelihoods to error size priors",
"save_every_n_iters": "frequency for saving model snapshots during fitting; if 0 only final state is saved",
"kappa_scan_target_duration": "target median syllable duration (in frames) for choosing kappa",
"whiten": "whether to whiten principal components; used to initialize the latent pose trajectory `x`",
"conf_threshold": "used to define outliers for interpolation when the model is initialized",
"conf_pseudocount": "pseudocount used regularize neural network confidences",
"fix_heading": "whether to keep the heading angle fixed; this should only be True if the pose is constrained to a narrow range of angles, e.g. a headfixed mouse.",
"fps": "frames per second of the video recordings",
}
sections = [
("ANATOMY", anatomy),
("FITTING", fitting),
("HYPER PARAMS", hypperams),
("OTHER", other),
]
with open(os.path.join(project_dir, "config.yml"), "w") as f:
f.write(_build_yaml(sections, comments))
def check_config_validity(config):
"""Check if the config is valid.
To be valid, the config must satisfy the following criteria:
- All the elements of `config["use_bodyparts"]` are also in
`config["bodyparts"]`
- All the elements of `config["anterior_bodyparts"]` are also in
`config["use_bodyparts"]`
- All the elements of `config["anterior_bodyparts"]` are also in
`config["use_bodyparts"]`
- For each pair in `config["skeleton"]`, both elements also in
`config["bodyparts"]`
Parameters
----------
config: dict
Returns
-------
validity: bool
"""
error_messages = []
# check anatomy
for bodypart in config["use_bodyparts"]:
if not bodypart in config["bodyparts"]:
error_messages.append(
f"ACTION REQUIRED: `use_bodyparts` contains {bodypart} "
"which is not one of the options in `bodyparts`."
)
for bodypart in sum(config["skeleton"], []):
if not bodypart in config["bodyparts"]:
error_messages.append(
f"ACTION REQUIRED: `skeleton` contains {bodypart} "
"which is not one of the options in `bodyparts`."
)
for bodypart in config["anterior_bodyparts"]:
if not bodypart in config["use_bodyparts"]:
error_messages.append(
f"ACTION REQUIRED: `anterior_bodyparts` contains {bodypart} "
"which is not one of the options in `use_bodyparts`."
)
for bodypart in config["posterior_bodyparts"]:
if not bodypart in config["use_bodyparts"]:
error_messages.append(
f"ACTION REQUIRED: `posterior_bodyparts` contains {bodypart} "
"which is not one of the options in `use_bodyparts`."
)
if len(error_messages) == 0:
return True
for msg in error_messages:
print(fill(msg, width=70, subsequent_indent=" "), end="\n\n")
return False
def load_config(project_dir, check_if_valid=True, build_indexes=True):
"""Load a project config file.
Parameters
----------
project_dir: str
Directory containing the config file
check_if_valid: bool, default=True
Check if the config is valid using
:py:func:`keypoint_moseq.io.check_config_validity`
build_indexes: bool, default=True
Add keys `"anterior_idxs"` and `"posterior_idxs"` to the config. Each
maps to a jax array indexing the elements of
`config["anterior_bodyparts"]` and `config["posterior_bodyparts"]` by
their order in `config["use_bodyparts"]`
Returns
-------
config: dict
"""
config_path = os.path.join(project_dir, "config.yml")
with open(config_path, "r") as stream:
config = yaml.safe_load(stream)
if check_if_valid:
check_config_validity(config)
if build_indexes:
config["anterior_idxs"] = jnp.array(
[config["use_bodyparts"].index(bp) for bp in config["anterior_bodyparts"]]
)
config["posterior_idxs"] = jnp.array(
[config["use_bodyparts"].index(bp) for bp in config["posterior_bodyparts"]]
)
if not "skeleton" in config or config["skeleton"] is None:
config["skeleton"] = []
return config
def update_config(project_dir, **kwargs):
"""Update the config file stored at `project_dir/config.yml`.
Use keyword arguments to update key/value pairs in the config. To update
model hyperparameters, just use the name of the hyperparameter as the
keyword argument.
Examples
--------
To update `video_dir` to `/path/to/videos`::
>>> update_config(project_dir, video_dir='/path/to/videos')
>>> print(load_config(project_dir)['video_dir'])
/path/to/videos
To update `trans_hypparams['kappa']` to `100`::
>>> update_config(project_dir, kappa=100)
>>> print(load_config(project_dir)['trans_hypparams']['kappa'])
100
"""
config = load_config(project_dir, check_if_valid=False, build_indexes=False)
config.update(kwargs)
generate_config(project_dir, **config)
def setup_project(
project_dir,
deeplabcut_config=None,
sleap_file=None,
nwb_file=None,
freipose_config=None,
dannce_config=None,
overwrite=False,
**options,
):
"""
Setup a project directory with the following structure::
project_dir
└── config.yml
Parameters
----------
project_dir: str
Path to the project directory (relative or absolute).
deeplabcut_config: str, default=None
Path to a deeplabcut config file. Will be used to initialize
`bodyparts`, `skeleton`, `use_bodyparts` and `video_dir` in the
keypoint MoSeq config (overrided by kwargs).
sleap_file: str, default=None
Path to a .hdf5 or .slp file containing predictions for one video. Will
be used to initialize `bodyparts`, `skeleton`, and `use_bodyparts` in
the keypoint MoSeq config (overrided by kwargs).
nwb_file: str, default=None
Path to a .nwb file containing predictions for one video. Will be used
to initialize `bodyparts`, `skeleton`, and `use_bodyparts` in the
keypoint MoSeq config. (overrided by kwargs).
freipose_config: str, default=None
Path to a freipose skeleton config file. Will be used to initialize
`bodyparts`, `skeleton`, and `use_bodyparts` in the keypoint MoSeq config
(overrided by kwargs).
dannce_config: str, default=None
Path to a dannce config file. Will be used to initialize `bodyparts`,
`skeleton`, and `use_bodyparts` in the keypoint MoSeq config (overrided
by kwargs).
overwrite: bool, default=False
Overwrite any config.yml that already exists at the path
`{project_dir}/config.yml`.
options
Used to initialize config file. Overrides default settings.
"""
if os.path.exists(project_dir) and not overwrite:
print(
fill(
f"The directory `{project_dir}` already exists. Use "
"`overwrite=True` or pick a different name"
)
)
return
if deeplabcut_config is not None:
dlc_options = {}
with open(deeplabcut_config, "r") as stream:
dlc_config = yaml.safe_load(stream)
if dlc_config is None:
raise RuntimeError(
f"{deeplabcut_config} does not exists or is not a"
" valid yaml file"
)
if "multianimalproject" in dlc_config and dlc_config["multianimalproject"]:
dlc_options["bodyparts"] = dlc_config["multianimalbodyparts"]
dlc_options["use_bodyparts"] = dlc_config["multianimalbodyparts"]
else:
dlc_options["bodyparts"] = dlc_config["bodyparts"]
dlc_options["use_bodyparts"] = dlc_config["bodyparts"]
dlc_options["skeleton"] = dlc_config["skeleton"]
dlc_options["video_dir"] = os.path.join(
dlc_config["project_path"], "videos"
)
options = {**dlc_options, **options}
elif sleap_file is not None:
sleap_options = {}
if os.path.splitext(sleap_file)[1] == ".slp":
slp_file = sleap_io.load_slp(sleap_file)
assert len(slp_file.skeletons) == 1, fill(
f"{sleap_file} contains more than one skeleton. "
"This is not currently supported. Please "
"open a github issue or email [email hidden]"
)
skeleton = slp_file.skeletons[0]
node_names = skeleton.node_names
edge_names = [[e.source.name, e.destination.name] for e in skeleton.edges]
else:
with h5py.File(sleap_file, "r") as f:
node_names = [n.decode("utf-8") for n in f["node_names"]]
edge_names = [
[n.decode("utf-8") for n in edge] for edge in f["edge_names"]
]
sleap_options["bodyparts"] = node_names
sleap_options["use_bodyparts"] = node_names
sleap_options["skeleton"] = edge_names
options = {**sleap_options, **options}
elif nwb_file is not None:
nwb_options = {}
with NWBHDF5IO(nwb_file, mode="r", load_namespaces=True) as io:
pose_obj = _load_nwb_pose_obj(io, nwb_file)
bodyparts = list(pose_obj.nodes[:])
nwb_options["bodyparts"] = bodyparts
nwb_options["use_bodyparts"] = bodyparts
if "edges" in pose_obj.fields:
edges = pose_obj.edges[:]
skeleton = [[bodyparts[i], bodyparts[j]] for i, j in edges]
nwb_options["skeleton"] = skeleton
options = {**nwb_options, **options}
elif freipose_config is not None:
freipose_options = {}
with open(freipose_config, "r") as stream:
freipose_config = commentjson.load(stream)
bodyparts = [kp for kp, color in freipose_config["keypoints"]]
skeleton = []
for [bp1, bp2], color in freipose_config["limbs"]:
if isinstance(bp1, list):
bp1 = bp1[0]
if isinstance(bp2, list):
bp2 = bp2[0]
skeleton.append(tuple(sorted([bodyparts[bp1], bodyparts[bp2]])))
freipose_options["bodyparts"] = bodyparts
freipose_options["use_bodyparts"] = bodyparts
freipose_options["skeleton"] = list(map(list, sorted(set(skeleton))))
options = {**freipose_options, **options}
elif dannce_config is not None:
dannce_config = loadmat(dannce_config)
bodyparts = [n[0][0].item() for n in dannce_config["joint_names"]]
skeleton = [
[bodyparts[i], bodyparts[j]] for i, j in dannce_config["joints_idx"] - 1
]
dannce_options = {
"bodyparts": bodyparts,
"use_bodyparts": bodyparts,
"skeleton": skeleton,
}
options = {**dannce_options, **options}
if not os.path.exists(project_dir):
os.makedirs(project_dir)
generate_config(project_dir, **options)
def save_pca(pca, project_dir, pca_path=None):
"""Save a PCA model to disk.
The model is saved to `pca_path` or else to `{project_dir}/pca.p`.
Parameters
----------
pca: :py:class:`sklearn.decomposition.PCA`
project_dir: str
pca_path: str, default=None
"""
if pca_path is None:
pca_path = os.path.join(project_dir, "pca.p")
joblib.dump(pca, pca_path)
def load_pca(project_dir, pca_path=None):
"""Load a PCA model from disk.
The model is loaded from `pca_path` or else from `{project_dir}/pca.p`.
Parameters
----------
project_dir: str
pca_path: str, default=None
Returns
-------
pca: :py:class:`sklearn.decomposition.PCA`
"""
if pca_path is None:
pca_path = os.path.join(project_dir, "pca.p")
assert os.path.exists(pca_path), fill(f"No PCA model found at {pca_path}")
return joblib.load(pca_path)
def load_checkpoint(project_dir=None, model_name=None, path=None, iteration=None):
"""Load data and model snapshot from a saved checkpoint.
The checkpoint path can be specified directly via `path` or else it is
assumed to be `{project_dir}/{model_name}/checkpoint.h5`.
Parameters
----------
project_dir: str, default=None
Project directory; used in conjunction with `model_name` to determine the
checkpoint path if `path` is not specified.
model_name: str, default=None
Model name; used in conjunction with `project_dir` to determine the
checkpoint path if `path` is not specified.
path: str, default=None
Checkpoint path; if not specified, the checkpoint path is set to
`{project_dir}/{model_name}/checkpoint.h5`.
iteration: int, default=None
Determines which model snapshot to load. If None, the last snapshot is
loaded.
Returns
-------
model: dict
Model dictionary containing states, parameters, hyperparameters,
noise prior, and random seed.
data: dict
Data dictionary containing observations, confidences, mask and
associated metadata (see :py:func:`keypoint_moseq.util.format_data`).
metadata: tuple (keys, bounds)
Recording names and start/end frames for the data (see
:py:func:`keypoint_moseq.util.format_data`).
iteration: int
Iteration of model fitting corresponding to the loaded snapshot.
"""
path = _get_path(project_dir, model_name, path, "checkpoint.h5")
with h5py.File(path, "r") as f:
saved_iterations = np.sort([int(i) for i in f["model_snapshots"]])
if iteration is None:
iteration = saved_iterations[-1]
else:
assert iteration in saved_iterations, fill(
f"No snapshot found for iteration {iteration}. "
f"Available iterations are {saved_iterations}"
)
model = load_hdf5(path, f"model_snapshots/{iteration}")
metadata = load_hdf5(path, "metadata")
data = load_hdf5(path, "data")
return model, data, metadata, iteration
def reindex_syllables_in_checkpoint(
project_dir=None, model_name=None, path=None, index=None, runlength=True
):
"""Reindex syllable labels by their frequency in the most recent model
snapshot in a checkpoint file.
This is an in-place operation: the checkpoint is loaded from disk, modified
and saved to disk again. The label permutation is applied to all model
snapshots in the checkpoint.
The checkpoint path can be specified directly via `path` or else it is
assumed to be `{project_dir}/{model_name}/checkpoint.h5`.
Parameters
----------
project_dir: str, default=None
model_name: str, default=None
path: str, default=None
index: array of shape (num_states,), default=None
Permutation for syllable labels, where `index[i]` is relabled as `i`.
If None, syllables are relabled by frequency, with the most frequent
syllable relabled as 0, and so on.
runlength: bool, default=True
If True, frequencies are quantified using the number of non-consecutive
occurrences of each syllable. If False, frequency is quantified by
total number of frames.
Returns
-------
index: array of shape (num_states,)
The index used for permuting syllable labels. If `index[i] = j`, then
the syllable formerly labeled `j` is now labeled `i`.
"""
path = _get_path(project_dir, model_name, path, "checkpoint.h5")
with h5py.File(path, "r") as f:
saved_iterations = [int(i) for i in f["model_snapshots"]]
if index is None:
with h5py.File(path, "r") as f:
last_iter = np.max(saved_iterations)
num_states = f[f"model_snapshots/{last_iter}/params/pi"].shape[0]
z = f[f"model_snapshots/{last_iter}/states/z"][()]
mask = f["data/mask"][()]
index = np.argsort(get_frequencies(z, mask, num_states, runlength))[::-1]
def _reindex(model):
model["params"]["betas"] = model["params"]["betas"][index]
model["params"]["pi"] = model["params"]["pi"][index, :][:, index]
model["params"]["Ab"] = model["params"]["Ab"][index]
model["params"]["Q"] = model["params"]["Q"][index]
# allocentric dynamics params (location-aware model) are also per-state
for key in ["delta_h", "sigmasq_h", "delta_v", "sigmasq_v"]:
if key in model["params"]:
model["params"][key] = model["params"][key][index]
model["states"]["z"] = np.argsort(index)[model["states"]["z"]]
return model
for iteration in tqdm.tqdm(
saved_iterations, desc="Reindexing", unit="model snapshot", ncols=72
):
model = load_hdf5(path, f"model_snapshots/{iteration}")
save_hdf5(
path,
_reindex(model),
f"model_snapshots/{iteration}",
exist_ok=True,
overwrite=True,
)
return index
def extract_results(
model,
metadata,
project_dir=None,
model_name=None,
save_results=True,
path=None,
overwrite=False,
):
"""Extract model outputs and [optionally] save them to disk.
Model outputs are saved to disk as a .h5 file, either at `path`
if it is specified, or at `{project_dir}/{model_name}/results.h5` if it is not.
If a .h5 file with the given path already exists, the outputs will be added
to it. The results have the following structure::
results.h5
├──recording_name1
│ ├──syllable # model state sequence (z), shape=(num_timepoints,)
│ ├──latent_state # model latent state (x), shape=(num_timepoints,latent_dim)
│ ├──centroid # model centroid (v), shape=(num_timepoints,keypoint_dim)
│ └──heading # model heading (h), shape=(num_timepoints,)
⋮
Parameters
----------
model : dict
Model dictionary containing states, parameters, hyperparameters,
noise prior, and random seed.
metadata: tuple (keys, bounds)
Recordings and start/end frames for the data (see
:py:func:`keypoint_moseq.util.format_data`).
save_results : bool, default=True
If True, the model outputs will be saved to disk.
project_dir : str, default=None
Path to the project directory. Required if `save_results=True` and
`results_path=None`.
model_name : str, default=None
Name of the model. Required if `save_results=True` and
`results_path=None`.
path : str, default=None
Optional path for saving model outputs.
overwrite : bool, default=False
If True, overwrite existing results for recordings that are
already in the results file. This is required when applying a
model to data that includes recordings from the original
training set.
Returns
-------
results_dict : dict
Dictionary of model outputs with the same structure as the results
`.h5` file.
"""
if save_results:
path = _get_path(project_dir, model_name, path, "results.h5")
# check for overlapping recording names
if not overwrite and os.path.exists(path):
recording_names = set(metadata[0])
with h5py.File(path, "r") as f:
overlap = recording_names & set(f.keys())
if overlap:
raise RuntimeError(fill(
f"{path} already contains results for "
f"{len(overlap)} recording(s), including "
f"'{next(iter(overlap))}'. To overwrite existing "
f"results, set overwrite=True in apply_model. "
f"See https://keypoint-moseq.readthedocs.io/en/"
f"latest/FAQs.html#detecting-existing-syllables"
f"-in-new-data for more information."
))
states = jax.device_get(model["states"])
# extract syllables; repeat first syllable an extra `nlags` times
nlags = states["x"].shape[1] - states["z"].shape[1]
z = np.pad(states["z"], ((0, 0), (nlags, 0)), mode="edge")
syllables = unbatch(z, *metadata)
# extract latent state, centroid, and heading
latent_state = unbatch(states["x"], *metadata)
centroid = unbatch(states["v"], *metadata)
heading = unbatch(states["h"], *metadata)
results_dict = {
recording_name: {
"syllable": syllables[recording_name],
"latent_state": latent_state[recording_name],
"centroid": centroid[recording_name],
"heading": heading[recording_name],
}
for recording_name in syllables.keys()
}
if save_results:
save_hdf5(path, results_dict, exist_ok=True, overwrite=overwrite)
print(fill(f"Saved results to {path}"))
return results_dict
def load_results(project_dir=None, model_name=None, path=None):
"""Load the results from a modeled dataset.
The results path can be specified directly via `path`. Otherwise it is
assumed to be `{project_dir}/{model_name}/results.h5`.
Parameters
----------
project_dir: str, default=None
model_name: str, default=None
path: str, default=None
Returns
-------
results: dict
See :py:func:`keypoint_moseq.fitting.apply_model`
"""
path = _get_path(project_dir, model_name, path, "results.h5")
return load_hdf5(path)
def save_results_as_csv(
results, project_dir=None, model_name=None, save_dir=None, path_sep="-"
):
"""Save modeling results to csv format.
This function creates a directory and then saves a separate csv file for
each recording. The directory is created at `save_dir` if provided,
otherwise at `{project_dir}/{model_name}/results`.
Parameters
----------
results: dict
See :py:func:`keypoint_moseq.io.extract_results`.
project_dir: str, default=None
Project directory; required if `save_dir` is not provided.
model_name: str, default=None
Name of the model; required if `save_dir` is not provided.
save_dir: str, default=None
Optional path to the directory where the csv files will be saved.
path_sep: str, default='-'
If a path separator ("/" or "\") is present in the recording name, it
will be replaced with `path_sep` when saving the csv file.
"""
save_dir = _get_path(project_dir, model_name, save_dir, "results", "save_dir")
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for key in tqdm.tqdm(results.keys(), desc="Saving to csv", ncols=72):
column_names, data = [], []
if "syllable" in results[key].keys():
column_names.append(["syllable"])
data.append(results[key]["syllable"].reshape(-1, 1))
if "centroid" in results[key].keys():
d = results[key]["centroid"].shape[1]
column_names.append(["centroid x", "centroid y", "centroid z"][:d])
data.append(results[key]["centroid"])
if "heading" in results[key].keys():
column_names.append(["heading"])
data.append(results[key]["heading"].reshape(-1, 1))
if "latent_state" in results[key].keys():
latent_dim = results[key]["latent_state"].shape[1]
column_names.append([f"latent_state {i}" for i in range(latent_dim)])
data.append(results[key]["latent_state"])
dfs = [pd.DataFrame(arr, columns=cols) for arr, cols in zip(data, column_names)]
df = pd.concat(dfs, axis=1)
for col in df.select_dtypes(include=[np.floating]).columns:
df[col] = df[col].astype(float).round(4)
save_name = key.replace(os.path.sep, path_sep)
save_path = os.path.join(save_dir, save_name)
df.to_csv(f"{save_path}.csv", index=False)
def _name_from_path(filepath, path_in_name, path_sep, remove_extension):
"""Create a name from a filepath.
Either return the name of the file (with the extension removed) or return
the full filepath, where the path separators are replaced with `path_sep`.
"""
if remove_extension:
filepath = os.path.splitext(filepath)[0]
if path_in_name:
return filepath.replace(os.path.sep, path_sep)
else:
return os.path.basename(filepath)
def save_keypoints(
save_dir, coordinates, confidences=None, bodyparts=None, path_sep="-"
):
"""Convenience function for saving keypoint detections to csv files.
One csv file is saved for each recording in `coordinates`. Each row in the
csv corresponds to one frame and the columns are named
"BODYPART1_x", "BODYPART1_y", "BODYPART1_conf", "BODYPART2_x", ...
Columns with confidence scores are ommitted if `confidences` is not provided.
Besides confidences, there can be 2 or 3 columns for each bodypart, depending
on whether the keypoints are 2D or 3D.
Parameters
----------
save_dir: str
Directory to save the results. A separate csv file will be saved for
each recording in `coordinates`.
coordinates: dict
Dictionary mapping recording names to numpy arrays of shape
(n_frames, n_keypoints, 2[or 3]) that contain the x and y (and z)
coordinates of the keypoints. If any keys contain a path separator
(such as "/"), it will be replaced with `path_sep` when naming the
csv file.
confidences: dict, default=None
Dictionary mapping recording names to numpy arrays of shape
(n_frames, n_keypoints) with the confidence scores of the keypoints.
Must have the same keys as `coordinates`.
bodyparts: list, default=None
List of bodypart names, in the same order as the keypoints in the
`coordinates` and `confidences` arrays. If None, the bodypart names
will be set to ["bodypart1", "bodypart2", ...].
path_sep: str, default='-'
If a path separator ("/" or "\") is present in the recording name, it
will be replaced with `path_sep` when saving the csv file.
"""
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if confidences is not None:
assert set(coordinates.keys()) == set(confidences.keys()), fill(
"The keys in `coordinates` and `confidences` must be the same."
)
# get number of keypoints and dimensions
_, num_keypoints, num_dims = next(iter(coordinates.values())).shape
# generate bodypart names if not provided
if bodyparts is None:
bodyparts = [f"bodypart{i}" for i in range(num_keypoints)]
# create column names
suffixes = ["x", "y", "z"][:num_keypoints]
if confidences is not None:
suffixes += ["conf"]
columns = [f"{bp}_{suffix}" for bp in bodyparts for suffix in suffixes]
# save data to csv
for recording_name, coords in coordinates.items():
if confidences is not None:
data = np.concatenate(
[coords, confidences[recording_name][..., np.newaxis]], axis=-1
).reshape(-1, (num_dims + 1) * num_keypoints)
else:
data = coords.reshape(-1, num_dims * num_keypoints)
save_name = recording_name.replace(os.path.sep, path_sep)
save_path = os.path.join(save_dir, save_name)
pd.DataFrame(data, columns=columns).to_csv(f"{save_path}.csv", index=False)
def load_keypoints(
filepath_pattern,
format,
extension=None,
recursive=True,
path_sep="-",
path_in_name=False,
remove_extension=True,
exclude_individuals=["single"],
):
"""
Load keypoint tracking results from one or more files. Several file
formats are supported:
- deeplabcut
.csv and .h5/.hdf5 files generated by deeplabcut. For single-animal
tracking, each file yields a single key/value pair in the returned
`coordinates` and `confidences` dictionaries. For multi-animal tracking,
a key/vaue pair will be generated for each tracked individual. For
example the file `two_mice.h5` with individuals "mouseA" and "mouseB"
will yield the pair of keys `'two_mice_mouseA', 'two_mice_mouseB'`.
- sleap
.slp and .h5/.hdf5 files generated by sleap. For single-animal tracking,
each file yields a single key/value pair in the returned `coordinates`
and `confidences` dictionaries. For multi-animal tracking, a key/vaue
pair will be generated for each track. For example a single file called
`two_mice.h5` will yield the pair of keys `'two_mice_track0',
'two_mice_track1'`.
- anipose
.csv files generated by anipose. Each file should contain five columns
per keypoint (x,y,z,error,score), plus a last column with the frame
number. The `score` column is used as the keypoint confidence.
- sleap-anipose
.h5/.hdf5 files generated by sleap-anipose. Each file should contain a
dataset called `'tracks'` with shape (n_frames, 1, n_keypoints, 3). If
there is also a `'point_scores'` dataset, it will be used as the
keypoint confidence. Otherwise, the confidence will be set to 1.
- nwb
.nwb files (Neurodata Without Borders). Each file should contain
exactly one `PoseEstimation` object (for multi-animal tracking, each
animal should be stored in its own .nwb file). The `PoseEstimation`
object should contain one `PoseEstimationSeries` object for each
bodypart. Confidence values are optional and will be set to 1 if not
present.
- facemap
.h5 files saved by Facemap. See Facemap documentation for details:
https://facemap.readthedocs.io/en/latest/outputs.html#keypoints-processing
The files should have the format::
[filename].h5
└──Facemap
├──keypoint1
│ ├──x
│ ├──y
│ └──likelihood
⋮
- freipose
.json files saved by FreiPose. Each file should contain a list of dicts
that each include a "kp_xyz" key with the 3D coordinates for one frame.
Keypoint scores (saved under "kp_score") are not loaded because they are
not bounded between 0 and 1, which is required for modeling. Since
FreiPose does not save the bodypart names, the `bodyparts` return
value is set to None.
- dannce
.mat files saved by Dannce.
Parameters
----------
filepath_pattern: str or list of str
Filepath pattern for a set of deeplabcut csv or hdf5 files, or a list
of such patterns. Filepath patterns can be:
- single file (e.g. `/path/to/file.csv`)
- single directory (e.g. `/path/to/dir/`)
- set of files (e.g. `/path/to/fileprefix*`)
- set of directories (e.g. `/path/to/dirprefix*`)
format: str
Format of the files to load. Must be one of `deeplabcut`, `sleap`,
`anipose`, or `sleap-anipose`.
extension: str, default=None
File extension to use when searching for files. If None, then the
extension will be inferred from the `format` argument:
- sleap: 'h5' or 'slp'
- deeplabcut: 'csv' or 'h5'
- anipose: 'csv'
- sleap-anipose: 'h5'
- nwb: 'nwb'
- facemap: 'h5'
- freipose: 'json'
- dannce: 'mat'
recursive: bool, default=True
Whether to search recursively for deeplabcut csv or hdf5 files.
path_in_name: bool, default=False
Whether to name the tracking results from each file by the path to the
file (True) or just the filename (False). If True, the `path_sep`
argument is used to separate the path components.
path_sep: str, default='-'
Separator to use when `path_in_name` is True. For example, if
`path_sep` is `'-'`, then the tracking results from the file
`/path/to/file.csv` will be named `path-to-file`. Using `'/'` as the
separator is discouraged, as it will cause problems saving/loading the
modeling results to/from hdf5 files.
remove_extension: bool, default=True
Whether to remove the file extension when naming the tracking results
from each file.
exclude_individuals: list of str, default=["single"]
List of individuals to exclude from the results. This is only used for
multi-animal tracking with deeplabcut.
Returns
-------
coordinates: dict
Dictionary mapping filenames to keypoint coordinates as ndarrays of
shape (n_frames, n_bodyparts, 2[or 3])
confidences: dict
Dictionary mapping filenames to `likelihood` scores as ndarrays of
shape (n_frames, n_bodyparts)
bodyparts: list of str
List of bodypart names. The order of the names matches the order of the
bodyparts in `coordinates` and `confidences`.
"""
formats = {
"deeplabcut": (_deeplabcut_loader, [".csv", ".h5", ".hdf5"]),
"sleap": (_sleap_loader, [".h5", ".hdf5", ".slp"]),
"anipose": (_anipose_loader, [".csv"]),
"sleap-anipose": (_sleap_anipose_loader, [".h5", ".hdf5"]),
"nwb": (_nwb_loader, [".nwb"]),
"facemap": (_facemap_loader, [".h5", ".hdf5"]),
"freipose": (_freipose_loader, [".json"]),
"dannce": (_dannce_loader, [".mat"]),
}
# get format-specific loader and extensions
assert format in formats, fill(
f"Unrecognized format '{format}'. Must be one of {list(formats.keys())}"
)
loader, extensions = formats[format]
# optionally override default extension list
if extension is not None:
extensions = [extension]
# optionally add format-specific arguments
if format == "deeplabcut":
additional_args = {"exclude_individuals": exclude_individuals}
else:
additional_args = {}
# get list of filepaths
filepaths = list_files_with_exts(filepath_pattern, extensions, recursive=recursive)
assert len(filepaths) > 0, fill(
f"No files with extensions {extensions} found for {filepath_pattern}"
)
# load keypoints from each file
coordinates, confidences, bodyparts = {}, {}, None
for filepath in tqdm.tqdm(filepaths, desc=f"Loading keypoints", ncols=72):
name = _name_from_path(filepath, path_in_name, path_sep, remove_extension)
try:
new_coordinates, new_confidences, bodyparts = loader(
filepath, name, **additional_args
)
except Exception as e:
print(fill(f"Error loading {filepath}"))
raise e
if set(new_coordinates.keys()) & set(coordinates.keys()):
raise ValueError(
f"Duplicate names found in {filepath_pattern}:\n\n"
f"{set(new_coordinates.keys()) & set(coordinates.keys())}"
f"\n\nThis may be caused by repeated filenames with "
"different extensions. If so, please set the extension "
"explicitly via the `extension` argument. Another possible"
" cause is commonly-named files in different directories. "
"if that is the case, then set `path_in_name=True`."
)
coordinates.update(new_coordinates)
confidences.update(new_confidences)
# check for valid results
assert len(coordinates) > 0, fill(f"No valid results found for {filepath_pattern}")
check_nan_proportions(coordinates, bodyparts)
return coordinates, confidences, bodyparts
def _deeplabcut_loader(filepath, name, exclude_individuals=["single"]):
"""Load tracking results from deeplabcut csv or hdf5 files."""
ext = os.path.splitext(filepath)[1]
if ext == ".h5":
df = pd.read_hdf(filepath)
if ext == ".csv":
with open(filepath) as f:
head = list(islice(f, 0, 5))
if "individuals" in head[1]:
header = [0, 1, 2, 3]
else:
header = [0, 1, 2]
df = pd.read_csv(filepath, header=header, index_col=0)
coordinates, confidences = {}, {}
if "individuals" in df.columns.names:
ind_bodyparts = {}
for ind in df.columns.get_level_values("individuals").unique():
if ind in exclude_individuals:
print(
f'Excluding individual: "{ind}". Set `exclude_individuals=[]` to include.'
)
else:
ind_df = df.xs(ind, axis=1, level="individuals")
bps = ind_df.columns.get_level_values("bodyparts").unique().tolist()
ind_bodyparts[ind] = bps
arr = ind_df.to_numpy().reshape(len(ind_df), -1, 3)
coordinates[f"{name}_{ind}"] = arr[:, :, :-1]
confidences[f"{name}_{ind}"] = arr[:, :, -1]
bodyparts = set(ind_bodyparts[list(ind_bodyparts.keys())[0]])
assert all([set(bps) == bodyparts for bps in ind_bodyparts.values()]), (
f"Bodyparts are not consistent across individuals. The following bodyparts "
f"were found for each individual: {ind_bodyparts}. Use `exclude_individuals`"
"to exclude specific individuals."
)
else:
bodyparts = df.columns.get_level_values("bodyparts").unique().tolist()
arr = df.to_numpy().reshape(len(df), -1, 3)
coordinates[name] = arr[:, :, :-1]
confidences[name] = arr[:, :, -1]
return coordinates, confidences, bodyparts
def _sleap_loader(filepath, name):
"""Load keypoints from sleap hdf5 or slp files."""
if os.path.splitext(filepath)[1] == ".slp":
slp_file = sleap_io.load_slp(filepath)
assert len(slp_file.skeletons) == 1, fill(
f"{filepath} contains more than one skeleton. "
"This is not currently supported. Please "
"open a github issue or email [email hidden]"
)
bodyparts = slp_file.skeletons[0].node_names
arr = slp_file.numpy(return_confidence=True)
coords = arr[:, :, :, :-1].transpose((1, 0, 2, 3))
confs = arr[:, :, :, -1].transpose((1, 0, 2))
else:
with h5py.File(filepath, "r") as f:
coords = f["tracks"][()].transpose((0, 3, 2, 1))
confs = f["point_scores"][()].transpose((0, 2, 1))
bodyparts = [name.decode("utf-8") for name in f["node_names"]]
if coords.shape[0] == 1:
coordinates = {name: coords[0]}
confidences = {name: confs[0]}
else:
coordinates = {f"{name}_track{i}": coords[i] for i in range(coords.shape[0])}
confidences = {f"{name}_track{i}": confs[i] for i in range(coords.shape[0])}
return coordinates, confidences, bodyparts
def _anipose_loader(filepath, name):
"""Load keypoints from anipose csv files."""
with open(filepath, "r") as f:
header = f.readline()
pattern = r"(?P<string>\w+)_x,(?P=string)_y,(?P=string)_z"
bodyparts = list(re.findall(pattern, header))
df = pd.read_csv(filepath)
coordinates = {
name: np.stack(
[df[[f"{bp}_x", f"{bp}_y", f"{bp}_z"]].to_numpy() for bp in bodyparts],
axis=1,
)
}
confidences = {name: df[[f"{bp}_score" for bp in bodyparts]].to_numpy()}
return coordinates, confidences, bodyparts
def _sleap_anipose_loader(filepath, name):
"""Load keypoints from sleap-anipose hdf5 files."""
with h5py.File(filepath, "r") as f:
coords = f["tracks"][()]
if "point_scores" in f.keys():
confs = f["point_scores"][()]
else:
confs = np.ones_like(coords[..., 0])
bodyparts = ["bodypart{}".format(i) for i in range(coords.shape[2])]
if coords.shape[1] == 1:
coordinates = {name: coords[:, 0]}
confidences = {name: confs[:, 0]}
else:
coordinates = {
f"{name}_track{i}": coords[:, i] for i in range(coords.shape[1])
}
confidences = {
f"{name}_track{i}": confs[:, i] for i in range(coords.shape[1])
}
return coordinates, confidences, bodyparts
def _load_nwb_pose_obj(io, filepath):
"""Grab PoseEstimation object from an opened .nwb file."""
all_objs = io.read().all_children()
pose_objs = [o for o in all_objs if isinstance(o, PoseEstimation)]
assert len(pose_objs) > 0, fill(f"No PoseEstimation objects found in {filepath}")
assert len(pose_objs) == 1, fill(
f"Found multiple PoseEstimation objects in {filepath}. "
"This is not currently supported. Please open a github "
"issue to request this feature."
)
pose_obj = pose_objs[0]
return pose_obj
def _nwb_loader(filepath, name):
"""Load keypoints from nwb files."""
with NWBHDF5IO(filepath, mode="r", load_namespaces=True) as io:
pose_obj = _load_nwb_pose_obj(io, filepath)
bodyparts = list(pose_obj.nodes[:])
coords = np.stack(
[pose_obj.pose_estimation_series[bp].data[()] for bp in bodyparts],
axis=1,
)
if "confidence" in pose_obj.pose_estimation_series[bodyparts[0]].fields:
confs = np.stack(
[
pose_obj.pose_estimation_series[bp].confidence[()]
for bp in bodyparts
],
axis=1,
)
else:
confs = np.ones_like(coords[..., 0])
coordinates = {name: coords}
confidences = {name: confs}
return coordinates, confidences, bodyparts
def _facemap_loader(filepath, name):
"""Load keypoints from facemap h5 files."""
with h5py.File(filepath, "r") as h5:
dset = h5["Facemap"]
bodyparts = sorted(dset.keys())
coords, confs = [], []
for bp in bodyparts:
coords.append(np.stack([dset[bp]["x"], dset[bp]["y"]], axis=1))
confs.append(dset[bp]["likelihood"])
coordinates = {name: np.stack(coords, axis=1)}
confidences = {name: np.stack(confs, axis=1)}
return coordinates, confidences, bodyparts
def _freipose_loader(filepath, name):
"""Load keypoints from freipose json files."""
with open(filepath, "r") as f:
data = json.load(f)
coords = np.concatenate([d["kp_xyz"] for d in data], axis=0)
coordinates = {name: coords}
confidences = {name: np.ones_like(coords[..., 0])}
return coordinates, confidences, None
def _dannce_loader(filepath, name):
"""Load keypoints from dannce mat files."""
mat = loadmat(filepath)
coords = mat["pred"].transpose((0, 2, 1))
confs = np.ones_like(coords[..., 0])
coordinates = {name: coords}
confidences = {name: confs}
return coordinates, confidences, None
def save_hdf5(filepath, save_dict, datapath=None, exist_ok=False, overwrite=False):
"""Save a dict of pytrees to an hdf5 file. The leaves of the pytrees must
be numpy arrays, scalars, or strings.
Parameters
----------
filepath: str
Path of the hdf5 file to create.
save_dict: dict
Dictionary where the values are pytrees, i.e. recursive collections of
tuples, lists, dicts, and numpy arrays.
datapath: str, default=None
Path within the hdf5 file to save the data. If None, the data is saved
at the root of the hdf5 file.
exist_ok: bool, default=False
If False, will raise an AssertionError when trying to modify an existing file.
overwrite: bool, default=False
If False, will raise an AssertionError when trying to overwrite an existing dataset or group.
"""
assert not (
os.path.exists(filepath) and not exist_ok
), f"{filepath} already exists. Set exist_ok to True to allow for editing an existing file."
with h5py.File(filepath, "a") as f:
if datapath is not None:
_savetree_hdf5(jax.device_get(save_dict), f, datapath, overwrite=overwrite)
else:
for k, tree in save_dict.items():
_savetree_hdf5(jax.device_get(tree), f, k, overwrite=overwrite)
def load_hdf5(filepath, datapath=None):
"""Load a dict of pytrees from an hdf5 file.
Parameters
----------
filepath: str
Path of the hdf5 file to load.
datapath: str, default=None
Path within the hdf5 file to load the data from. If None, the data is
loaded from the root of the hdf5 file.
Returns
-------
save_dict: dict
Dictionary where the values are pytrees, i.e. recursive collections of
tuples, lists, dicts, and numpy arrays.
"""
with h5py.File(filepath, "r") as f:
if datapath is None:
return {k: _loadtree_hdf5(f[k]) for k in f}
else:
return _loadtree_hdf5(f[datapath])
def _savetree_hdf5(tree, group, name, overwrite=False):
"""Recursively save a pytree to an h5 file group.
Parameters
----------
tree: pytree
The pytree to save.
group: h5py.Group
The h5 file group to save the pytree to.
name: str
The key in 'group' where the pytree will be saved.
overwrite: bool, default=False
If True, will overwrite an existing dataset or group.
"""
assert not (
not overwrite and name in group
), f"{name} already exists in {group}. Set overwrite to True to overwrite data in an existing file."
if name in group:
del group[name]
if isinstance(tree, np.ndarray):
if tree.dtype.kind == "U":
dt = h5py.special_dtype(vlen=str)
group.create_dataset(name, data=tree.astype(object), dtype=dt)
else:
group.create_dataset(name, data=tree)
elif isinstance(tree, (float, int, str)):
group.create_dataset(name, data=tree)
else:
subgroup = group.create_group(name)
subgroup.attrs["type"] = type(tree).__name__
if isinstance(tree, (tuple, list)):
for k, subtree in enumerate(tree):
_savetree_hdf5(subtree, subgroup, f"arr{k}", overwrite=overwrite)
elif isinstance(tree, dict):
for k, subtree in tree.items():
_savetree_hdf5(subtree, subgroup, k, overwrite=overwrite)
else:
raise ValueError(f"Unrecognized type {type(tree)}")
def _loadtree_hdf5(leaf):
"""Recursively load a pytree from an h5 file group."""
if isinstance(leaf, h5py.Dataset):
data = np.array(leaf[()])
if h5py.check_dtype(vlen=data.dtype) == str:
data = np.array([item.decode("utf-8") for item in data])
elif data.dtype.kind == "S":
data = data.item().decode("utf-8")
elif data.shape == ():
data = data.item()
return data
else:
leaf_type = leaf.attrs["type"]
values = map(_loadtree_hdf5, leaf.values())
if leaf_type == "dict":
return dict(zip(leaf.keys(), values))
elif leaf_type == "list":
return list(values)
elif leaf_type == "tuple":
return tuple(values)
else:
raise ValueError(f"Unrecognized type {leaf_type}")

io.py at commit d676308, under other · at the source

Overview

Authors: M. Natalie Davis^1,2,3, Mary Bullock^1,2,3, Aanishaa Jhaldiyal^1,2,3, Niya Holifield⁴, Karim Mikhail^1,2,3, Yuliya Voskobiynyk^1,2,3, Rachael M. Vollmer^1,2,3, George C. Prendergast⁵, Kelli Lauderdale⁶, Jorge J. Palop⁶, Karen L. Gamble^3,4, Erik D. Roberson^1,2,3

ORCID iDs: M. Natalie Davis, Aanishaa Jhaldiyal, Yuliya Voskobiynyk, Erik D. Roberson

Department of Neurology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama 35294
Killion Center for Neurodegeneration and Experimental Therapeutics, The University of Alabama at Birmingham, Birmingham, Alabama 35294
Alzheimer’s Disease Center, The University of Alabama at Birmingham, Birmingham, Alabama 35294
Department of Psychiatry, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama 35294
Lankenau Institute for Medical Research, Wynnewood, Pennsylvania 19096
Gladstone Institute of Neurological Disease, San Francisco, California 94158

Institutions: University of Alabama at Birmingham (United States); UAB Medicine (United States); Lankenau Institute for Medical Research (United States); Gladstone Institutes (United States)

Journal: eNeuro, volume 13, issue 3, pages ENEURO.0304-25.2026

Dates: received 28 July 2025; accepted 26 February 2026; published online 25 March 2026; in print March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1523/eneuro.0304-25.2026 · PMID 41895857 · PMCID PMC13064429 · OpenAlex W7141567197

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: EEG (modality), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)

Methods: Smoothing, state filtering, decompositions, Statistics, Preprocessing, Spectral & time-frequency, Connectivity, fMRI & imaging, Physiology & signal measures

Keywords: Alzheimer's disease, BIN1, network hyperexcitability, parvalbumin

MeSH: Adaptor Proteins, Signal Transducing*, Alzheimer Disease*, Brain*, Cognition*, Neurons*, Nuclear Proteins*, Parvalbumins*, Tumor Suppressor Proteins*, Animals, Disease Models, Animal, Electroencephalography, Exploratory Behavior, Female, Male, Mice, Mice, Knockout, Nerve Tissue Proteins (* major topic)

Journal subjects: Research Article: Negative Results, Disorders of the Nervous System

Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: HHS | National Institutes of Health (NIH) (RF1AG059405, R01AG061785, P30AG086401, T32HD071866, T32NS061788)

Citations: not cited yet (Europe PMC); 65 references in the paper

Research resources: NeuN RRID:AB_11205760, anti-GAPDH RRID:AB_2107445, BIN1 RRID:AB_2243396, parvalbumin RRID:AB_2927389, immunoblotted with anti-BIN1 99D RRID:AB_309738, RRID:IMSR_JAX:00377, 2007) were crossed with PV-Cre mice RRID:IMSR_JAX:017320, a small group of hAPPJ20 mice RRID:MMRRC_034836-JAX, This model was run in RStudio RRID:SCR_000432, EthoVision XT RRID:SCR_000441, The Spike2 Software RRID:SCR_000903, RRID:SCR_002798, RRID:SCR_007358, RRID:SCR_013715, RRID:SCR_014296, RRID:SCR_014574, RRID:SCR_017107, RRID:SCR_020317, RRID:SCR_021391, RRID:SCR_021496, RRID:SCR_022918, RRID:SCR_023765, RRID:SCR_024783, RRID:SCR_025032, RRID:SCR_025401

Abstract

Bridging integrator 1 (BIN1) is one of the strongest genetic risk factors for Alzheimer’s disease (AD), yet its function in the brain and role in AD remain unclear. Neuronal BIN1 isoform levels are decreased in AD, and recent data show an important role of BIN1 in inhibitory neurons. Inhibitory neurons are key regulators of cognition and network excitability, with parvalbumin-expressing (PV) neurons as the most abundant subtype. We tested the hypothesis that loss of BIN1 from PV neurons contributes to AD-related cognitive dysfunction and network hyperexcitability. We generated a cell type–specific conditional knock-out mouse line, Bin1-pvKO, and examined mice of both sexes. These mice showed few behavioral differences when assessed with traditional or machine learning–based behavioral tests, with only a slight reduction in exploratory behavior in aged cohorts. Bin1-pvKO mice showed no significant differences in network excitability on measures of induced seizure susceptibility and spiking on cortical electroencephalographic recordings. Finally, Bin1-pvKO mice exhibited no major differences in power spectral analysis of cortical electroencephalographic recordings, with only a modest reduction in delta power at high activity levels. These findings suggest that BIN1 loss in PV neurons alone is insufficient to drive the cognitive and network dysfunction observed in AD models. While these results do not exclude a role of BIN1 in PV neurons in AD models, if combined with a “second hit” or alterations in other cell types, they indicate that BIN1 loss in PV neurons alone does not recapitulate key AD-related phenotypes.

Reproduced under the paper's license (CC BY), from the paper cited above.

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robersonlab-uab/Parvalbumin-BIN1-2026

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 8e5668b4783bde40cf095fb9b1f44160edb37cf4, 20 March 2026

Size: 2 files, 0 scripts

Software Heritage: not archived

Found in: the text, “Single-molecule fluorescent in situ hybridizatio”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

1 file

README.md, Text, 22 lines

At the source: github.com/robersonlab-uab/Parvalbumin-BIN1-2026.git

dattalab/keypoint-moseq

License: other

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: d67630857635bd9febb8cc5d46310604b01e7dc6, 9 September 2026

Languages: Python (12), Jupyter (3)

Size: 40 files, 15 scripts

Software Heritage: archived

Found in: the text, “Behavioral clustering”

Holds: README, license file, environment (setup.cfg, setup.py), tests, documentation, 3 notebooks

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Tools: NumPy (8 files), Matplotlib (6 files), h5py (4 files), JAX (4 files), SciPy (4 files), pandas (3 files), imageio (1 file), NetworkX (1 file), Neurodata Without Borders (PyNWB, MatNWB) (1 file), OpenCV (1 file), Pillow (1 file), Plotly (1 file), scikit-learn (1 file), seaborn (1 file), statsmodels (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

17 files

docs/keypoint_moseq_colab.ipynb, Jupyter, 389 lines
docs/source/analysis.ipynb, Jupyter, 163 lines
docs/source/conf.py, Python, 81 lines
docs/source/modeling.ipynb, Jupyter, 348 lines
keypoint_moseq/__init__.py, Python, 26 lines
keypoint_moseq/_version.py, Python, 695 lines
keypoint_moseq/analysis.py, Python, 1,754 lines
keypoint_moseq/calibration.py, Python, 467 lines
keypoint_moseq/fitting.py, Python, 678 lines
keypoint_moseq/io.py, Python, 1,428 lines, 1 match
keypoint_moseq/util.py, Python, 1,797 lines, 1 match
keypoint_moseq/viz.py, Python, 3,054 lines
setup.py, Python, 11 lines
tests/test_outlier_removal.py, Python, 246 lines
versioneer.py, Python, 2,274 lines
LICENSE.md, License, 49 lines
README.md, Text, 24 lines

At the source: github.com/dattalab/keypoint-moseq/

Code accessibility

The code described in the paper is freely available online (https://doi.org/10.17504/protocols.io.bp2l6dzbzvqe/v1).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 4 keywords, 17 MeSH terms, 1 funder, 62 references, 25 RRIDs.

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Davis, M. N., Bullock, M., Jhaldiyal, A., Holifield, N., Mikhail, K., Voskobiynyk, Y., Vollmer, R. M., Prendergast, G. C., Lauderdale, K., Palop, J. J., Gamble, K. L., & Roberson, E. D. (2026). Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene BIN1 Is Insufficient to Drive Cognitive or Network Excitability Changes. eNeuro, 13(3), ENEURO.0304-25.2026. https://doi.org/10.1523/eneuro.0304-25.2026

BibTeX

@article{davis2026parvalbumin, author = {Davis, M. Natalie and Bullock, Mary and Jhaldiyal, Aanishaa and Holifield, Niya and Mikhail, Karim and Voskobiynyk, Yuliya and Vollmer, Rachael M. and Prendergast, George C. and Lauderdale, Kelli and Palop, Jorge J. and Gamble, Karen L. and Roberson, Erik D.}, title = {{Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene \<i\>BIN1\</i\> Is Insufficient to Drive Cognitive or Network Excitability Changes}}, journal = {eNeuro}, year = {2026}, month = mar, volume = {13}, number = {3}, pages = {ENEURO.0304--25.2026}, publisher = {Society for Neuroscience}, issn = {2373-2822}, doi = {10.1523/eneuro.0304-25.2026}, url = {https://doi.org/10.1523/eneuro.0304-25.2026}, pmid = {41895857}, pmcid = {PMC13064429} }

RIS

TY - JOUR AU - Davis, M. Natalie AU - Bullock, Mary AU - Jhaldiyal, Aanishaa AU - Holifield, Niya AU - Mikhail, Karim AU - Voskobiynyk, Yuliya AU - Vollmer, Rachael M. AU - Prendergast, George C. AU - Lauderdale, Kelli AU - Palop, Jorge J. AU - Gamble, Karen L. AU - Roberson, Erik D. TI - Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene BIN1 Is Insufficient to Drive Cognitive or Network Excitability Changes T2 - eNeuro J2 - eNeuro PY - 2026 DA - 2026/03/27 VL - 13 IS - 3 SP - ENEURO.0304 EP - 25.2026 SN - 2373-2822 PB - Society for Neuroscience DO - 10.1523/eneuro.0304-25.2026 UR - https://doi.org/10.1523/eneuro.0304-25.2026 LA - en ER -

CSL-JSON

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Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene <i>BIN1</i> Is Insufficient to Drive Cognitive or Network Excitability Changes.

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Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene <i>BIN1</i> Is Insufficient to Drive Cognitive or Network Excitability Changes.